Author: Derrick D. Jackson
Title: Founder & Senior Director of Cloud Security Architecture & Risk
Credentials: CISSP, CRISC, CCSP
Last updated October 31th, 2025
Table of Contents
What Are AI Hallucinations?
When Air Canada’s chatbot fabricated a bereavement fare policy in 2024, the company was held legally liable. That case set a clear precedent: you’re responsible for what your AI systems confidently state as fact, even when it’s completely false.
The stakes keep rising. In October 2025, Deloitte agreed to reimburse the Australian government after AI-generated hallucinations appeared in a $440,000 consulting report. The firm had to return fees and provide additional work at no cost.
This isn’t rare. 72% of organizations have already adopted generative AI in at least one business function, McKinsey found in 2025. Adoption is accelerating. So is exposure to AI confabulation: AI generating plausible but false information with complete confidence.
Executive Summary: What Business Leaders Must Know
- The Risk: AI hallucination rates vary significantly by task complexity. This is as low as 3% for certain structured queries to much higher rates for open-ended or knowledge-intensive tasks. Systems confidently fabricate facts, citations, and statistics without warning.
- The Cost: Legal liability (Air Canada precedent), reputational damage (top S&P 500 disclosure concern), operational drag (verification overhead negating productivity gains).
- The Control: Structured measurement (Hallucination@k, incident density), RAG architecture grounding, and human-in-the-loop validation for high-stakes decisions.
Whether you’re using ChatGPT, Copilot, or deploying enterprise AI, this risk affects you.
AI HALLUCINATIONS by Tech JacksWhat Is a Hallucination vs. What Is Confabulation? Understanding AI’s False Outputs
The industry has used “hallucination” for years, but NIST’s AI Risk Management Framework deliberately chose “confabulation” as the more accurate term. Here’s why it matters.
Hallucination in psychiatry means perceiving something that isn’t there (a sensory experience). AI systems don’t have sensory perception. They process text and predict statistically likely responses.
Confabulation in psychology means filling memory gaps with fabricated information, without intent to deceive. This precisely describes what large language models (LLMs) do. When faced with incomplete knowledge, they generate plausible-sounding text based on learned patterns, even if factually wrong.
Both terms describe the same problem: AI confidently producing false information. ISO/IEC 42001, the first international AI management standard, explicitly identifies this as a unique risk requiring systematic controls.
Why AI Hallucination Matters Now
Three factors converge.
Legal liability is real. The Air Canada case proved companies can’t hide behind “the AI made a mistake.”
Reputational damage tops risk lists. An analysis of S&P 500 filings found reputational risk is the most cited AI concern. Eleven companies specifically called out hallucinations as threats to credibility.
Regulations are here. The EU AI Act imposes binding accuracy requirements. NIST’s Generative AI Profile is becoming the U.S. standard.
How AI Hallucinates: The Technical Mechanism Behind False Outputs
AI models predict the next most likely word. That’s it. They’re trained on massive datasets to learn statistical patterns, not facts. When you ask about something outside their training data, they don’t admit uncertainty. They extrapolate from patterns. This is how AI hallucinates (not from malice, but from fundamental design).
Two error types:
Intrinsic confabulations contradict information in the prompt. If you ask an AI to summarize a document and it includes contradictory details, that’s intrinsic.
Extrinsic confabulations can’t be verified against external sources. The AI invents research papers, cites non-existent legal cases, or fabricates statistics. More dangerous because they seem authoritative.
How to Measure AI Hallucination Risk: Board-Level Metrics
Track these six KPIs like you track uptime: SIDGS
1. Hallucination@k (H@k) – Error rate: percentage of responses containing unsupported claims. Track it like manufacturing defects.
2. Source Attribution Rate – Percentage with verifiable citations. Shows whether AI confabulation is actually being prevented.
3. Verification Coverage – Percentage checked by fact-checkers before reaching users. Higher coverage means lower exposure.
4. Abstention Rate – How often the model refuses to answer rather than guessing. Higher is better.
5. Escalation & MTTR – Percentage routed to humans and mean time to resolve. Rising escalation signals accuracy problems.
6. Incident Density – AI hallucination incidents per 1,000 queries by severity. Your uptime equivalent for AI trust.
Put these on the same dashboards as uptime and ROI.
Quick Actions You Can Take Today:
- Verify everything important: Ask your AI tools for sources and citations on every critical claim. If it can’t provide them, don’t trust the output.
- Never skip human review: Don’t deploy AI-generated content in high-stakes situations without expert validation.
- Set up feedback loops: Create easy ways for users to flag incorrect outputs. This catches problems your automated testing misses.
Frequently Asked Questions
What are the actual business risks?
Financial loss happens when AI misinformation drives decisions. Fabricated demand forecasts trigger costly orders. Hallucinated risk models misprice loans. Deloitte had to reimburse $440,000 after AI hallucinations appeared in a government consulting report.
Operational drag occurs when verification takes longer than the original task. You’re paying for AI to create more work.
Legal exposure extends beyond chatbots. In 2023, lawyers were sanctioned for submitting a brief with fictional case citations. The citations looked real but didn’t exist.
How do you detect AI confabulations?
Technical metrics measure accuracy during development. Groundedness scores assess whether outputs base responses only on provided information. Evaluation error rates track mistakes against test datasets.
User feedback is critical. Direct ratings, escalation rates, and reported inaccuracies reveal AI hallucination patterns that automated testing misses.
Automated monitoring compares outputs against source data in real-time, flagging unsupported claims before they reach users.
What’s the best prevention strategy?
Retrieval-Augmented Generation (RAG) is your primary defense. RAG retrieves verified information from trusted databases and forces AI to base responses on that context, significantly reducing extrinsic confabulations.
Prompt Engineering matters more than you’d think. Chain-of-thought prompting and explicit source citation instructions can improve accuracy substantially.
Human-in-the-Loop validation isn’t optional for high-stakes work. Route low-confidence outputs to experts before acting.
Runtime Monitoring tools from NEC and Microsoft’s VeriTrail flag unsupported claims in real-time.
Clear Governance starts at planning. Confabulation risk should drive use case selection.
What regulations apply?
The EU AI Act (effective August 2024) imposes binding accuracy requirements. AI confabulations in high-risk systems mean substantial penalties.
NIST’s AI Risk Management Framework explicitly identifies confabulation as a risk requiring systematic management. It’s becoming the U.S. standard.
ISO/IEC 42001 certification proves you have auditable processes to manage AI hallucination risks globally.
Who’s responsible when AI confabulates?
Gartner research shows 28% of organizations assign AI governance to the CEO. Top-level ownership correlates with better outcomes.
A RACI matrix clarifies roles: Chief AI Officer owns governance, Product Managers handle implementations, Data Science validates training, Legal reviews compliance.
What should executives prioritize?
Risk assessment first. Not every task needs AI. High-stakes decisions requiring 100% accuracy need deterministic systems.
Governance before scaling. ISO 42001 and NIST frameworks give you structure. Skipping governance creates regulatory exposure.
Continuous monitoring. Performance drifts as data changes. Undetected AI hallucinations accumulate fast.
Internal literacy. Teams must understand: AI optimizes for plausibility, not truth.
Key Takeaways
AI confabulation isn’t a bug. It’s how these systems work. You manage it through technical controls (RAG, monitoring), governance (clear ownership), and strategic discipline (right use cases).
Companies building this capability early win trust and competitive advantage. Those that don’t face consequences. Understanding this risk means deploying AI responsibly.
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